The following paragraphs are provided by way of background to the present disclosure. They are not however an admission that anything discussed therein is prior art or part of the knowledge of persons of skill in the art.
Digital image data, such as that typically required for digital mapping, may be collected using a variety of different data collection techniques. When data representing the same underlying objects of interest are collected at multiple times and/or by sensors with different (spatial, spectral, temporal, radiometric) resolutions, and then compared with each other, locational discrepancies frequently arise between two or more collected datasets. Such discrepancies may arise, for example, as a result of the use of different data collection, or data processing techniques, inadvertent or inherent error in data collection techniques, and variations with respect to the prevalent environmental conditions when, where and how the data were collected.
By way of example, the data collection efforts associated with using the GIS roof-top polygons derived from a lidar survey, or from stereo air-photography as a mask to extract the N hottest locations (commonly referred to as hot-spots) from a high-resolution thermal infrared image of the same rooftops (Hay et al, 2011) are frequently challenging to reconcile. In this case, the GIS polygons and raster thermal image may have been created, and/or collected at different times, under different conditions, from different sensors and platforms, under different spatial, spectral, and radiometric resolutions, each with their own inherent spatial errors. Thus, even though each dataset may have very similar inherent locational information, such as that captured from a global positioning system (GPS), their combined inherent spatial errors (derived independently from each acquisition platform as well as human error in the case of deriving GIS polygons) will require the non-trivial task of precisely geometrically correct each roof-top polygon to its equivalent thermal roof-top image-object, from which roof hot-spot digital numbers (DNs) can be accurately defined. For example, at a sub-meter spatial resolution, the inherent sensor error is typically 1-2 pixels. Thus, if a roof polygon is misaligned only one pixel (thus, 1 m in this example) with the corresponding thermal roof-top pixels, it could result in the defined hot-spot DNs and their locations actually representing ground pixels, or other warmer non-roof pixels (i.e., trees, grass, etc.) located 1 m away from the roof edge. If this geometric problem remains uncorrected for many 100's of 1000's of roof-tops, then the validity of the defined hot-spots is highly questionable, resulting in limited utility. Thus there is a need to automatically reduce the inherent fine spatial error between such diverse datasets; which if successful would represent an important more spatially precise way to extract location based information, from two or more different datasets.
There are many possible causes for locational discrepancies, commonly referred to as geometric errors, when producing detailed geographical maps from data acquired from unmanned aerial systems (UAS), fixed-wing aircraft airborne or satellite platforms. Discrepancies between overlapping objects of interest in two or more images, may arise as a result of different data collection methodologies, for instance, the use of different cameras with different spectral resolutions, or different camera viewing and acquisition angles. Geometric errors may also arise as a result of inadvertent platform movement associated with air turbulence, or as a result of different lighting, or weather conditions at the time of collecting the different images (Rahman et al., 2013).
In general, geometric errors may be grouped into two main categories: systematic errors and non-systematic errors. Systematic errors are typically repeatable errors caused by systematic distortions in the sensor/platform, including but not limited to scan skew, mirror scan velocity variance, platform velocity and earth rotation (Richards, 2013, p. 50-53), for example. These can typically be corrected for the entire image with mathematical formulas. Non-systematic errors, however are often more difficult to correct. These represent unforeseen changes in the sensors geometry, such as altitude variance and platform attitude (roll, pitch and yaw). They may also result from relief displacement. These errors are typically corrected by collecting specific ground control points (GCPs), and then applying image rectification methods (Richards, 2013, p. 56-57).
Methods of image rectification are commonly referred to as geometric correction methods, which are commonly subdivided in two main classes: image-to-map correction methods, and image-to-image correction methods, including both paper and digital maps with GPS coordinates (Richards, 2013 p. 57-72). In general, geometric correction process involve: (i) establishing GCPs that match the location of pixels composing the objects of interest to known locations (e.g.: on a map, or to the same objects of interest in both images); (ii) applying a mathematical model to establish the relationship between the objects of interest and their known-location (on a map, or the overlapping images); (iii) transforming the image (and related objects of interest) to the new projected geometry; and (iv) interpolating the digital numbers (i.e. pixels) composing the objects of interest into new (re-oriented) digital numbers. While geometric correction involves a family of methods for correcting images into related map coordinates (Richards, 2013 p. 59-61), it is often impractical for accurately correcting the individual non-systematic errors of large numbers of individual objects of interest over very large areas. For example, these methods are not suitable for efficiently and accurately representing the boundary location of many 100s of 1000's or millions of buildings in a large city, within an error margin of less than 1 meter. Manually geometrically correcting each building would be an extremely labor intensive and uneconomical process. This is in part, because the overall image accuracy is dependent on the geometric accuracy of the individual GCPs—which are time consuming to accurately collect and because the collection process is prone to human error. Additionally, once the geometric transformation is applied to the entire image, the accuracy of the location of an individual object of interest (and their associated GCPs) is influenced by the accuracy and location of its neighboring GCPs. Thus it is possible for geometric transformations to actually reduce both the individual and overall locational accuracy of the objects of interest.
In order to improve upon non-systematic errors inherent within two or more high-resolution images of the same location, that are required to be geometrically corrected together, the user would need to provide detailed locational improvements to large numbers of diverse objects of interest within the image-to-image geometric correction process, also referred to as geometric fitting. Geometric fitting represents the attribution of more detailed locational information to each object of interest within a scene than the general geographic information that typically accompanies an image (e.g. latitudes/longitudes, datum, etc). This is both time-consuming and would require the error-free manual collection of many additional samples. When applied to very large, detailed and complex datasets these requirements are operationally impractical.
Thus there is a need in the art for processes to accurately and efficiently geometrically fit individual objects of interest that exist within comparable separate datasets.